Online data services have stringent performance requirement and must tolerate workload fluctuation. This paper introduces P it S top , a new query language runtime design built on the idea of interruptible query processing : the time-consuming task of data inspection for processing each query or update may be interrupted and resumed later at the boundary of fine-grained data partitions. This counter-intuitive idea enables a novel form of fine-grained concurrency while preserving sequential consistency . We build P it S top through modifying the language runtime of Cypher, the query language of a state-of-the-art graph database, Neo4j. Our evaluation on the Google Cloud shows that P it S top can outperform unmodified Neo4j during workload fluctuation, with reduced latency and increased throughput.

Power modeling is an essential building block for computer systems in support of energy optimization, energy profiling, and energy-aware application development. We introduce Vesta , a novel approach to modeling the power consumption of applications with one key insight: language runtime events are often correlated with a sustained level of power consumption. When compared with the established approach of power modeling based on hardware performance counters (HPCs), Vesta has the benefit of solely requiring application-scoped information and enabling a higher level of explainability, while achieving comparable or even higher precision. Through experiments performed on 37 real-world applications on the Java Virtual Machine (JVM), we find the power model built by Vesta is capable of predicting energy consumption with a mean absolute percentage error of 1.56 % , while the monitoring of language runtime events incurs small performance and energy overhead.